[Paper Review] Small Profits and Quick Returns: A Practical SocialWelfare Maximizing Incentive Mechanism for Deadline-Sensitive Tasks in Crowdsourcing

Duin Back, Bong Jun Choi+1 more|arXiv (Cornell University)|Jun 30, 2017

Mobile Crowdsensing and Crowdsourcing5 references3 citations

TL;DR

This paper proposes a practical Expected Social Welfare Maximizing (ESWM) incentive mechanism for deadline-sensitive crowdsourcing tasks, modeling heterogeneous provider punctuality and task value depreciation to maximize expected social welfare via a greedy heuristic. Simulation results show ESWM achieves higher expected social welfare and platform utility than the benchmark by attracting more participants, despite similar average participant utility due to increased competition.

ABSTRACT

As the driving force of crowdsourcing is the interaction among participants, various incentive mechanisms have been proposed to attract sufficient participants. However, the existing works assume that all the providers always meet the deadline and the task value accordingly remains constant. To bridge the gap of such impractical assumption, we model the heterogeneous punctuality behavior of providers and the task value depreciation of requesters. Based on those models, we propose an Expected Social Welfare Maximizing (ESWM) mechanism that aims to maximize the expected social welfare in polynomial time. Simulation results show that our heuristic-based mechanism achieves higher expected social welfare and platform utility via attracting more participants.

Motivation & Objective

• To address the impractical assumption in existing incentive mechanisms that all providers always meet deadlines.
• To model heterogeneous punctuality behavior of providers and task value depreciation after deadlines.
• To design a practical, polynomial-time incentive mechanism that maximizes expected social welfare under realistic constraints.
• To improve platform utility and social welfare by attracting more participants through better utility estimation.

Proposed method

• Model provider punctuality as a probabilistic distribution, capturing heterogeneous on-time performance.
• Formulate task valuation depreciation over time, with requester-specific depreciation rates.
• Define expected social welfare as the sum of expected task values minus provider costs, weighted by match probabilities.
• Propose a greedy heuristic-based algorithm to solve the NP-complete expected social welfare maximization problem in polynomial time.
• Integrate reselection dynamics where participants choose mechanisms based on average utility, simulating real-world competition.
• Use binary integer programming as the optimal baseline, but solve via approximation to ensure scalability.

Experimental results

Research questions

• RQ1 How does task value depreciation after deadlines affect social welfare and incentive design in crowdsourcing?
• RQ2 Can a heuristic mechanism achieve higher expected social welfare than existing benchmarks while remaining computationally efficient?
• RQ3 How does participant reselection based on average utility impact platform performance and mechanism attractiveness?
• RQ4 To what extent does modeling heterogeneous provider punctuality improve social welfare and platform utility?
• RQ5 What trade-offs exist between social welfare, platform utility, and average participant utility in deadline-sensitive crowdsourcing?

Key findings

• The ESWM mechanism achieves significantly higher expected social welfare and platform utility than the benchmark, as shown in Figures 2a and 2b.
• The ESWM mechanism attracts more participants due to better utility estimation, increasing the likelihood of matching with high-quality providers.
• Despite attracting more participants, the average requester and provider utility in ESWM remains nearly identical to the benchmark, due to increased competition.
• The reselection process stabilizes once average utilities balance, indicating no further significant shift in participant distribution between mechanisms.
• The ESWM mechanism supports more task requests than the benchmark, demonstrating scalability and practical viability.
• The heuristic-based approach achieves near-optimal performance in polynomial time, making it suitable for real-world deployment.